This week you are going to learn how to map your data. Continuing with our previous theme of international migration and moving into this week’s theme of the Great Migration, we will make two sets of maps of the United States for the censuses of 1900, 1930, 1960, and 1990. One set of maps will show the proportion of each state’s population that was born outside of the United States (foreign-born divided by total). The other set will show the proportion of each state’s population that was classified as African American (African American divided by total). By the time you finish this notebook, in addition to knowing how to visualize data in a map, you will also know how to join data frames and recode continuous variables into ordinal variables.

Getting started

Start by installing and loading the libraries we will need (tidyverse, sf, tigris, viridis, cowplot) and reading in the data for this week. Remember to install any that you have not used previously. We will only be working with the 48 contiugous states, so remove records for Alaska and Hawaii and any rows where STATEFIP is missing (> 56).

library(tidyverse)
library(sf)
library(tigris)
library(viridis)
library(cowplot)
library(ggplot2)

ipums <- read_csv("wk8.csv") %>% filter(!STATEFIP %in% c(2,15) & STATEFIP <= 56)

Explore the data to see which variables and samples we are working with.

names(ipums)
[1] "YEAR"     "STATEFIP" "PERWT"    "RACE"     "RACED"    "BPL"      "BPLD"    
for (year in unique(ipums$YEAR)) {
  print(year)
  print(summary(filter(ipums, YEAR == year)$PERWT))
}
[1] 1900
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
    0.0   100.0   100.0   100.1   101.0   101.0 
[1] 1930
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  65.40   99.65  100.75  100.90  101.95  278.85 
[1] 1960
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  99.00   99.00  100.00   99.61  100.00  100.00 
[1] 1990
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   0.00   14.00   19.00   19.85   25.00  345.00 

Calculating percentages

We will start by calculating the quantities we want to map: the percentage of each state’s population in each year that was born outside the United States and the percentage of each state’s population in each year that was classified as African American.

First we want to add to our ipums data frame two new variables: one called FBORN that indicates whether a given person was born outside the United States, and another called AfAm that indicates whether a given person was classified as African American. These variables will take values of TRUE and FALSE. Test to make sure that you have constructed these variables correctly.

ipums <- ipums %>% mutate(FBORN = BPL >= 150, AfAm = RACE == 2)

table(ipums$BPL, ipums$FBORN)
     
        FALSE    TRUE
  1    317645       0
  2      8596       0
  4    102760       0
  5    201255       0
  6    935904       0
  8    147681       0
  9    172918       0
  10    33542       0
  11    70979       0
  12   308663       0
  13   408094       0
  15    16827       0
  16    59883       0
  17   802171       0
  18   393839       0
  19   269261       0
  20   197091       0
  21   316616       0
  22   296673       0
  23    90204       0
  24   222383       0
  25   411302       0
  26   584105       0
  27   310012       0
  28   239891       0
  29   390109       0
  30    56138       0
  31   141348       0
  32    25717       0
  33    54361       0
  34   408572       0
  35    83145       0
  36  1246114       0
  37   423677       0
  38    69911       0
  39   750849       0
  40   225116       0
  41   118591       0
  42   968866       0
  44    65783       0
  45   233418       0
  46    69191       0
  47   340971       0
  48   890553       0
  49   104115       0
  50    40623       0
  51   335957       0
  53   196097       0
  54   186006       0
  55   345754       0
  56    27196       0
  90       47       0
  99    47917       0
  100     487       0
  105    2656       0
  110   57812       0
  115    1796       0
  120    3213       0
  150       0   78011
  155       0       1
  160       0    1648
  199       0      79
  200       0  230926
  210       0   55642
  250       0   37434
  260       0   51184
  300       0   50542
  400       0    6000
  401       0    3850
  402       0     517
  403       0       2
  404       0   10647
  405       0   16747
  410       0   46192
  411       0   13697
  412       0    2235
  413       0    5542
  414       0   38912
  419       0       4
  420       0    3337
  421       0   11281
  422       0       7
  423       0     282
  424       0      15
  425       0    8448
  426       0    5050
  429       0       2
  430       0     466
  431       0       2
  432       0      40
  433       0   11966
  434       0   65834
  435       0     508
  436       0   13276
  437       0      21
  438       0    6064
  439       0       1
  450       0   13970
  451       0     585
  452       0   13104
  453       0  108840
  454       0   11831
  455       0   41879
  456       0    6672
  457       0   10158
  458       0     101
  459       0       2
  460       0     629
  461       0    1963
  462       0    4702
  463       0       4
  465       0   37760
  499       0     355
  500       0   46137
  501       0   20470
  502       0   29921
  510       0      24
  511       0    5733
  512       0    2344
  513       0    8152
  514       0    1451
  515       0   45184
  516       0     669
  517       0    5491
  518       0   26081
  519       0      12
  520       0    1312
  521       0   28056
  522       0   10076
  524       0     102
  530       0      66
  531       0     480
  532       0    1928
  534       0    5462
  535       0    1457
  536       0     384
  537       0    4429
  538       0      26
  539       0      38
  540       0     800
  541       0    2589
  542       0    4162
  543       0      91
  544       0     227
  545       0      28
  546       0      17
  547       0      32
  548       0     258
  549       0      16
  599       0     307
  600       0   16890
  700       0    3799
  710       0    2283
  800       0       2
  900       0   45221
table(ipums$RACE, ipums$AfAm)
   
       FALSE     TRUE
  1 13582101        0
  2        0  1696059
  3   118369        0
  4    78039        0
  5    33416        0
  6   205511        0
  7   468080        0

Now create a two new data frames, one called fb and one called aa. Each data frame will have one row for each state in each year. The fb data frame will include a column called FB that indicates the percent of the state’s population that was born outside the United States. The aa data frame will include a column called AA that indicates the percent of the state’s population that was classified as African American.

fb <- ipums %>% group_by(STATEFIP,YEAR) %>% count(FBORN,wt=PERWT) %>% mutate(TOTAL = sum(n)) %>% filter(FBORN) %>% mutate(FB = n/TOTAL) %>% select(-FBORN,-n,-TOTAL) %>% ungroup
Error: Must group by variables found in `.data`.
* Column `FBORN` is not found.
Run `rlang::last_error()` to see where the error occurred.

Working with maps

To map data, we need to start with a data frame that contains the outline of the geography we are working with. In this case, we want a map that outlines the states of the United States. That map exists in the tigris package, and we can use the states() function to get it. We specify that we want the data frame to be an object of class sf, which stands for simple features, a standard for geographic boundary files. We will call the new data frame states.

states <- states(class = "sf")

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head(states)
Simple feature collection with 6 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -97.23909 ymin: 24.39631 xmax: -71.08857 ymax: 49.38448
Geodetic CRS:  NAD83
  REGION DIVISION STATEFP  STATENS GEOID STUSPS          NAME LSAD MTFCC FUNCSTAT        ALAND      AWATER    INTPTLAT
1      3        5      54 01779805    54     WV West Virginia   00 G4000        A  62266231560   489271086 +38.6472854
2      3        5      12 00294478    12     FL       Florida   00 G4000        A 138947364717 31362872853 +28.4574302
3      2        3      17 01779784    17     IL      Illinois   00 G4000        A 143779863817  6215723896 +40.1028754
4      2        4      27 00662849    27     MN     Minnesota   00 G4000        A 206230065476 18942261495 +46.3159573
5      3        5      24 01714934    24     MD      Maryland   00 G4000        A  25151726296  6979340970 +38.9466584
6      1        1      44 01219835    44     RI  Rhode Island   00 G4000        A   2677787140  1323663210 +41.5974187
      INTPTLON                       geometry
1 -080.6183274 MULTIPOLYGON (((-81.74725 3...
2 -082.4091477 MULTIPOLYGON (((-86.38865 3...
3 -089.1526108 MULTIPOLYGON (((-91.18529 4...
4 -094.1996043 MULTIPOLYGON (((-96.78438 4...
5 -076.6744939 MULTIPOLYGON (((-77.45881 3...
6 -071.5272723 MULTIPOLYGON (((-71.7897 41...

We can plot this empty map with ggplot() using the geom_sf() function. The theme_map() layer from the cowplot package and the coord_sf(datum = NA) layer will remove the axes and gridlines.

ggplot(states) + geom_sf() +
  theme_map() + coord_sf(datum = NA)

Joining data frames

To place our data on this map, we need to use a join() function to link the states data frame to the data frames that contain the data we want to map.

There are four kinds of join() functions. To see how these work, run the following code block to make two separate data frames.

name <- c("Abby", "Carmen", "Diego", "Gertrude", "Hector", "Irene")
dogs <- c(2, 5, 3, 1, 1, 2)
dog_data <- data.frame(name, dogs)

name <- c("Bob", "Carmen", "Diego", "Evelyn", "Frank", "Hector")
cats <- c(2, 2, 3, 1, 1, 1)
cat_data <- data.frame(name, cats)

dog_data
cat_data

As you can see, we have six dog owners and six cat owners, and some of them are the same people. There are four different types of joins. In a left_join() and a right_join(), the first data frame listed is the left data frame and the second is the right data frame. In a left_join(), the final data frame will include all rows from the left data frame and only matching rows from the right data frame. In a right_join(), the final data frame will include all rows from the right data frame and only matching rows from the left data frame. So if we want a data frame that only includes dog owners but indicates both the number of dogs they have and the number of cats they have, we can use either a left_join() or a right_join().

dog_owners <- left_join(dog_data, cat_data)
Joining, by = "name"
dog_owners

dog_owners <- right_join(cat_data, dog_data)
Joining, by = "name"
dog_owners

By default, R will join on any column or set of columns that is identical between the two data frames. We can also specify how we want R to match up the data frames, which we will need to do when we join our data to our map.

If we want the equivalent for cat owners, we just reverse the order of the data frames.

cat_owners <- left_join(cat_data, dog_data)
Joining, by = "name"
cat_owners

cat_owners <- right_join(dog_data, cat_data)
Joining, by = "name"
cat_owners

The inner_join() function returns a data frame with only the matching rows of the two data frames that are joined. We would use this if we want a data frame including only people who own both dogs and cats.

dog_cat_owners <- inner_join(dog_data, cat_data)
Joining, by = "name"
dog_cat_owners

The full_join() function returns a data frame containing all rows of both data frames. We would use this to get a data frame containing everyone who owns either a dog or a cat or both.

pet_owners <- full_join(dog_data, cat_data)
Joining, by = "name"
pet_owners

All of these joins are dplyr functions, so we can also use pipes:

dog_owners <- dog_data %>% left_join(cat_data) # same as left_join(dog_data, cat_data)
Joining, by = "name"
cat_owners <- dog_data %>% right_join(cat_data) # same as right_join(dog_data, cat_data)
Joining, by = "name"
dog_cat_owners <- dog_data %>% inner_join(cat_data) # same as inner_join(dog_data, cat_data)
Joining, by = "name"
pet_owners <- dog_data %>% full_join(cat_data) # same as full_join(dog_data, cat_data)
Joining, by = "name"
pet_owners
dog_cat_owners

Joining data to a map

Now we can join our two data frames (fb and aa) to our map. We will use an inner join, so the result will include only rows that are common to both the data frame and the map. The column that is common to the data frames we are joining is the one with the state fips code. This is called STATEFIP in fb and aa and called STATEFP in states. The STATEFP variable in states is a character, but the STATEFIP variable in fb and aa is numeric. We will solve this incompatibility by first creating a new column in states called STATEFIP that is numeric. After we join, we need to use the st_as_sf() function from the sf package to restore the geographic properties of the resulting data frame.

states$STATEFIP <- as.numeric(states$STATEFP)
fb_map <- inner_join(fb, states) %>% st_as_sf()
Joining, by = "STATEFIP"
aa_map <- inner_join(aa, states) %>% st_as_sf()
Joining, by = "STATEFIP"
head(fb_map)
Simple feature collection with 6 features and 17 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -114.8163 ymin: 30.14442 xmax: -84.88825 ymax: 37.00372
Geodetic CRS:  NAD83
head(aa_map)
Simple feature collection with 6 features and 17 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -114.8163 ymin: 30.14442 xmax: -84.88825 ymax: 37.00372
Geodetic CRS:  NAD83

Now we can adjust the code from our empty state map to map values of FB and AA and facet by YEAR.

map_fb <- ggplot(fb_map,aes(fill=FB)) + geom_sf() +
            theme_map() + coord_sf(datum = NA) + facet_wrap(vars(YEAR))

map_aa <- ggplot(aa_map,aes(fill=AA)) + geom_sf() +
            theme_map() + coord_sf(datum = NA) + facet_wrap(vars(YEAR))

map_fb

map_aa

Continuous color ramps

There are a few problems with these maps. First, lower values are mapping darker, which is the opposite of what we want. General principles of visual data literacy suggest that larger values should map darker. Second, the PCT variable is continuous, so the fill color is appearing on a continuous scale (changing gradually), which makes it hard to see what the actual values are. We can address these issues one at a time.

To change the colors to light –> dark, we can specify our own color ramp with the scale_fill_gradient() layer. The parameters are the color we want for the lowest value and the color we want for the highest value. Here we will use scale_fill_gradient() on the map showing percent of population classified as African American.

map_aa + scale_fill_gradient(low = "white", high = "darkslategray4")

Alternatively, the viridis package provides nice continuous color ramps for maps. The folloiwing code shows an application of the viridis package on the map showing percent of population born outside the United States.


map_fb + scale_fill_viridis(option = "magma")

You can use a number of premade color ramps through the viridis package. Here I am using “magma” but you can see others here: https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html


map_fb + scale_fill_viridis(option = "viridis")

The viridis package also allows you to specify whether smaller numbers should be lighter or darker: “direction = -1” allows me to make my smaller numbers lighter.


map_fb + scale_fill_viridis(option = "viridis", direction = -1)

Discrete color ramps

Mapping on a continuous scale emphasizes similarity from state to state. To emphasize difference, we can map on a discrete scale. To do that, we will need to choose bins to sort our states into. This is similar to choosing binwidth when making a histogram. One way to choose bins is to say that we want x categories, with a roughly-equal number of states in each.

Here we will make 6 categories for the map of percent African American, but you can make any number of categories by changing the value of classes.

classes <- 6
labels <- c()
quantiles <- quantile(aa_map$AA, probs = seq(0, 1, 1/classes))
for (i in 1:length(quantiles)) {
  labels <- c(labels, paste0(round(quantiles[i]*100, 1), "% - ", round(quantiles[i+1]*100, 1), "%"))
}
labels <- labels[-length(labels)]
aa_map$AAD <- cut(aa$AA, breaks = quantiles, labels = labels, include.lowest = TRUE)
ggplot(data = aa_map, aes(fill = AAD)) + geom_sf() +
  theme_map(font_size = 70) + coord_sf(datum = NA) +
  facet_wrap(vars(YEAR), ncol = 2) +
  labs(title = "Percent of Population Classified as African American, by State",
       fill = "Percent of State Population") +
  scale_fill_viridis(option = "magma", discrete = T, direction = -1)

Alternatively, we can choose where we want the breaks to be and how we want to label them. Here we will do that for the foreign-born population.

breaks <- c(0, .015, .03, .06, .12, .24, 1)
labels <- c()
for (i in 1:length(breaks)) {
  labels <- c(labels, paste0(breaks[i]*100, "% - ", breaks[i+1]*100, "%"))
}
labels <- labels[-length(labels)]
labels[length(labels)] <- paste0(">", breaks[length(breaks)-1] * 100, "%")
fb_map$FBD <- cut(fb$FB, breaks = breaks, labels = labels, include.lowest = TRUE)
ggplot(data = fb_map, aes(fill = FBD)) + geom_sf() +
  theme_map(font_size = 70)  + coord_sf(datum = NA) +
  facet_wrap(vars(YEAR), ncol = 2) +
  labs(title = "Percent of Population Born Outside the U.S., by State",
       fill = "Percent of State Population") +
  scale_fill_viridis(option = "magma", discrete = T, direction = -1)

You are now ready for Lab 8!

---
title: "STS 112: Notebook 8"
author: "Mel Avina-Beltran"
output: html_notebook
---

This week you are going to learn how to map your data. Continuing with our previous theme of international migration and moving into this week's theme of the Great Migration, we will make two sets of maps of the United States for the censuses of 1900, 1930, 1960, and 1990. One set of maps will show the proportion of each state's population that was born outside of the United States (foreign-born divided by total). The other set will show the proportion of each state's population that was classified as African American (African American divided by total). By the time you finish this notebook, in addition to knowing how to visualize data in a map, you will also know how to join data frames and recode continuous variables into ordinal variables.

## Getting started
Start by installing and loading the libraries we will need (tidyverse, sf, tigris, viridis, cowplot) and reading in the data for this week. Remember to install any that you have not used previously. We will only be working with the 48 contiugous states, so remove records for Alaska and Hawaii and any rows where `STATEFIP` is missing (> 56).
```{r results = "hide"}
library(tidyverse)
library(sf)
library(tigris)
library(viridis)
library(cowplot)
library(ggplot2)

ipums <- read_csv("wk8.csv") %>% filter(!STATEFIP %in% c(2,15) & STATEFIP <= 56)
```
Explore the data to see which variables and samples we are working with.
```{r}
names(ipums)
for (year in unique(ipums$YEAR)) {
  print(year)
  print(summary(filter(ipums, YEAR == year)$PERWT))
}
```
## Calculating percentages
We will start by calculating the quantities we want to map: the percentage of each state's population in each year that was born outside the United States and the percentage of each state's population in each year that was classified as African American.

First we want to add to our `ipums` data frame two new variables: one called `FBORN` that indicates whether a given person was born outside the United States, and another called `AfAm` that indicates whether a given person was classified as African American. These variables will take values of `TRUE` and `FALSE`. Test to make sure that you have constructed these variables correctly.
```{r}
ipums <- ipums %>% mutate(FBORN = BPL >= 150, AfAm = RACE == 2)

table(ipums$BPL, ipums$FBORN)
table(ipums$RACE, ipums$AfAm)
```
Now create a two new data frames, one called `fb` and one called `aa`. Each data frame will have one row for each state in each year. The `fb` data frame will include a column called `FB` that indicates the percent of the state's population that was born outside the United States. The `aa` data frame will include a column called `AA` that indicates the percent of the state's population that was classified as African American.
```{r}
fb <- ipums %>% group_by(STATEFIP,YEAR) %>% count(FBORN,wt=PERWT) %>% mutate(TOTAL = sum(n)) %>% filter(FBORN) %>% mutate(FB = n/TOTAL) %>% select(-FBORN,-n,-TOTAL) %>% ungroup
aa <- ipums %>% group_by(STATEFIP,YEAR) %>% count(AfAm, wt=PERWT) %>% mutate(TOTAL = sum(n)) %>% filter(AfAm) %>% mutate(AA=n/TOTAL) %>% select(-AfAm,-n,-TOTAL) %>% ungroup

head(fb)
head(aa)
```
## Working with maps
To map data, we need to start with a data frame that contains the outline of the geography we are working with. In this case, we want a map that outlines the states of the United States. That map exists in the `tigris` package, and we can use the `states()` function to get it. We specify that we want the data frame to be an object of class `sf`, which stands for **simple features**, a standard for geographic boundary files. We will call the new data frame `states`.
```{r}
states <- states(class = "sf")
head(states)
```
We can plot this empty map with `ggplot()` using the `geom_sf()` function. The `theme_map()` layer from the `cowplot` package and the `coord_sf(datum = NA)` layer will remove the axes and gridlines.
```{r}
ggplot(states) + geom_sf() +
  theme_map() + coord_sf(datum = NA)
```

## Joining data frames
To place our data on this map, we need to use a `join()` function to link the `states` data frame to the data frames that contain the data we want to map.

There are four kinds of `join()` functions. To see how these work, run the following code block to make two separate data frames.
```{r}
name <- c("Abby", "Carmen", "Diego", "Gertrude", "Hector", "Irene")
dogs <- c(2, 5, 3, 1, 1, 2)
dog_data <- data.frame(name, dogs)

name <- c("Bob", "Carmen", "Diego", "Evelyn", "Frank", "Hector")
cats <- c(2, 2, 3, 1, 1, 1)
cat_data <- data.frame(name, cats)

dog_data
cat_data
```
As you can see, we have six dog owners and six cat owners, and some of them are the same people. There are four different types of joins. In a `left_join()` and a `right_join()`, the first data frame listed is the left data frame and the second is the right data frame. In a `left_join()`, the final data frame will include all rows from the left data frame and only matching rows from the right data frame. In a `right_join()`, the final data frame will include all rows from the right data frame and only matching rows from the left data frame. So if we want a data frame that only includes dog owners but indicates both the number of dogs they have and the number of cats they have, we can use either a `left_join()` or a `right_join()`.
```{r}
dog_owners <- left_join(dog_data, cat_data)
dog_owners

dog_owners <- right_join(cat_data, dog_data)
dog_owners
```
By default, R will join on any column or set of columns that is identical between the two data frames. We can also specify how we want R to match up the data frames, which we will need to do when we join our data to our map.

If we want the equivalent for cat owners, we just reverse the order of the data frames.
```{r}
cat_owners <- left_join(cat_data, dog_data)
cat_owners

cat_owners <- right_join(dog_data, cat_data)
cat_owners
```
The `inner_join()` function returns a data frame with only the matching rows of the two data frames that are joined. We would use this if we want a data frame including only people who own both dogs and cats.
```{r}
dog_cat_owners <- inner_join(dog_data, cat_data)
dog_cat_owners
```
The `full_join()` function returns a data frame containing all rows of both data frames. We would use this to get a data frame containing everyone who owns either a dog or a cat or both.
```{r}
pet_owners <- full_join(dog_data, cat_data)
pet_owners
```
All of these joins are `dplyr` functions, so we can also use pipes:
```{r}
dog_owners <- dog_data %>% left_join(cat_data) # same as left_join(dog_data, cat_data)
cat_owners <- dog_data %>% right_join(cat_data) # same as right_join(dog_data, cat_data)
dog_cat_owners <- dog_data %>% inner_join(cat_data) # same as inner_join(dog_data, cat_data)
pet_owners <- dog_data %>% full_join(cat_data) # same as full_join(dog_data, cat_data)

pet_owners
dog_cat_owners
```

## Joining data to a map
Now we can join our two data frames (`fb` and `aa`) to our map. We will use an inner join, so the result will include only rows that are common to both the data frame and the map. The column that is common to the data frames we are joining is the one with the state fips code. This is called `STATEFIP` in `fb` and `aa` and called `STATEFP` in `states`. The `STATEFP` variable in `states` is a character, but the `STATEFIP` variable in `fb` and `aa` is numeric. We will solve this incompatibility by first creating a new column in `states` called `STATEFIP` that is numeric. After we join, we need to use the `st_as_sf()` function from the `sf` package to restore the geographic properties of the resulting data frame.
```{r}
states$STATEFIP <- as.numeric(states$STATEFP)
fb_map <- inner_join(fb, states) %>% st_as_sf()
aa_map <- inner_join(aa, states) %>% st_as_sf()

head(fb_map)
head(aa_map)
```
Now we can adjust the code from our empty state map to map values of FB and AA and facet by `YEAR`.
```{r}
map_fb <- ggplot(fb_map,aes(fill=FB)) + geom_sf() +
            theme_map() + coord_sf(datum = NA) + facet_wrap(vars(YEAR))

map_aa <- ggplot(aa_map,aes(fill=AA)) + geom_sf() +
            theme_map() + coord_sf(datum = NA) + facet_wrap(vars(YEAR))

map_fb
map_aa
```
## Continuous color ramps
There are a few problems with these maps. First, lower values are mapping darker, which is the opposite of what we want. General principles of visual data literacy suggest that larger values should map darker. Second, the `PCT` variable is continuous, so the fill color is appearing on a continuous scale (changing gradually), which makes it hard to see what the actual values are. We can address these issues one at a time.

To change the colors to light --> dark, we can specify our own color ramp with the `scale_fill_gradient()` layer. The parameters are the color we want for the lowest value and the color we want for the highest value. Here we will use `scale_fill_gradient()` on the map showing percent of population classified as African American.
```{r}
map_aa + scale_fill_gradient(low = "white", high = "darkslategray4")
```
Alternatively, the `viridis` package provides nice continuous color ramps for maps. The folloiwing code shows an application of the `viridis` package on the map showing percent of population born outside the United States.
```{r}

map_fb + scale_fill_viridis(option = "magma")

```
You can use a number of premade color ramps through the `viridis` package. Here I am using "magma" but you can see others here: https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html
```{r}

map_fb + scale_fill_viridis(option = "viridis")

```
The `viridis` package also allows you to specify whether smaller numbers should be lighter or darker: "direction = -1" allows me to make my smaller numbers lighter.
```{r}

map_fb + scale_fill_viridis(option = "viridis", direction = -1)

```

## Discrete color ramps
Mapping on a continuous scale emphasizes similarity from state to state. To emphasize difference, we can map on a discrete scale. To do that, we will need to choose **bins** to sort our states into. This is similar to choosing binwidth when making a histogram. One way to choose bins is to say that we want x categories, with a roughly-equal number of states in each.

Here we will make 6 categories for the map of percent African American, but you can make any number of categories by changing the value of `classes`.
```{r fig.height=15}
classes <- 6
labels <- c()
quantiles <- quantile(aa_map$AA, probs = seq(0, 1, 1/classes))
for (i in 1:length(quantiles)) {
  labels <- c(labels, paste0(round(quantiles[i]*100, 1), "% - ", round(quantiles[i+1]*100, 1), "%"))
}
labels <- labels[-length(labels)]
aa_map$AAD <- cut(aa$AA, breaks = quantiles, labels = labels, include.lowest = TRUE)
ggplot(data = aa_map, aes(fill = AAD)) + geom_sf() +
  theme_map(font_size = 70) + coord_sf(datum = NA) +
  facet_wrap(vars(YEAR), ncol = 2) +
  labs(title = "Percent of Population Classified as African American, by State",
       fill = "Percent of State Population") +
  scale_fill_viridis(option = "magma", discrete = T, direction = -1)
```
Alternatively, we can choose where we want the breaks to be and how we want to label them. Here we will do that for the foreign-born population.
```{r fig.height=15}
breaks <- c(0, .015, .03, .06, .12, .24, 1)
labels <- c()
for (i in 1:length(breaks)) {
  labels <- c(labels, paste0(breaks[i]*100, "% - ", breaks[i+1]*100, "%"))
}
labels <- labels[-length(labels)]
labels[length(labels)] <- paste0(">", breaks[length(breaks)-1] * 100, "%")
fb_map$FBD <- cut(fb$FB, breaks = breaks, labels = labels, include.lowest = TRUE)
ggplot(data = fb_map, aes(fill = FBD)) + geom_sf() +
  theme_map(font_size = 70)  + coord_sf(datum = NA) +
  facet_wrap(vars(YEAR), ncol = 2) +
  labs(title = "Percent of Population Born Outside the U.S., by State",
       fill = "Percent of State Population") +
  scale_fill_viridis(option = "magma", discrete = T, direction = -1)
```

You are now ready for Lab 8!